Performance evaluation of channel estimation based on adaptive filters and adaptive guard interval for mobile WiMAX

2012 ◽  
Author(s):  
Lien Pham-Hong ◽  
Quang Nguyen-Duc ◽  
Tra Luu-Thanh
Keyword(s):  
Performance Evaluation ◽  
Channel Estimation ◽  
Adaptive Filters ◽  
Mobile Wimax ◽  
Guard Interval

scholarly journals Performance evaluation of adaptive filters for sparse wireless channel estimation

2017 ◽  
Author(s):  
Markus V. S. Lima ◽  
Tadeu N. Ferreira ◽  
Wallace A. Martins ◽  
Marcele O. K. Mendonca ◽  
Paulo S. R. Diniz
Keyword(s):  
Performance Evaluation ◽  
Channel Estimation ◽  
Adaptive Filters ◽  
Wireless Channel

A Combined Algorithm of Kalman Estimator and Guard Interval Optimization for Mobile WiMAX

2013 ◽  
Vol 4 (1) ◽  
pp. 16-28 ◽  
Author(s):  
Quang Nguyen-Duc ◽  
Lien Pham-Hong ◽  
Thang Nguyen-Manh ◽  
Tra Luu-Thanh
Keyword(s):  
Channel Estimation ◽  
Fading Channels ◽  
Communication Systems ◽  
Wireless Communication Systems ◽  
Mobile Wimax ◽  
Interval Optimization ◽  
Estimation Algorithms ◽  
Guard Interval ◽  
Combined Algorithm ◽  
Wimax System

Mobile WiMAX (Worldwide Interoperability for Microwave Access) system has been recently applied widely in wireless communication systems. In this paper, the channel estimation algorithms were studied for the mobile WiMAX system. The comb-type pilot was used for channel estimation algorithms. The authors proposed an adaptive algorithm of channel estimation based on Kalman filter which had good performance in fading channels. Then, based on the result of channel estimation, we proposed an advanced algorithm of GI (Guard Interval) optimization. The results showed that the Kalman estimator combined with GI optimization algorithm showed the best performance in this paper. This algorithm was verified by computer simulation.

scholarly journals Adaptive Filtering Based on Legendre Polynomials

2020 ◽  
Author(s):  
Lu Shen ◽  
Yuriy Zakharov ◽  
Long Shi ◽  
Benjamin Henson
Keyword(s):  
Channel Estimation ◽  
Interference Cancellation ◽  
Legendre Polynomials ◽  
Adaptive Filters ◽  
Recursive Least Squares ◽  
Full Duplex ◽  
Estimation Accuracy ◽  
Underwater Acoustic Communications ◽  
Basis Expansion ◽  
New Family

Abstract:<div><br><div><pre><p>In system identification scenarios, classical adaptive filters, such as the recursive least squares (RLS) algorithm, predict the system impulse response. If a tracking delay is acceptable, interpolating estimators capable of providing more accurate estimates of time-varying impulse responses can be used; channel estimation in communications is an example of such applications. The basis expansion model (BEM) approach is known to be efficient for non-adaptive (block) channel estimation in communications. In this paper, we combine the BEM approach with the sliding-window RLS (SRLS) algorithm and propose a new family of adaptive filters. Specifically, we use the Legendre polynomials, thus the name the SRLS-L adaptive filter. The identification performance of the SRLS-L algorithm is evaluated analytically and via simulation. The analysis shows significant improvement in the estimation accuracy compared to the SRLS algorithm and a good match between the theoretical and simulation results. The performance is further investigated in application to the self-interference cancellation in full-duplex underwater acoustic communications, where a high estimation accuracy is required. A field experiment conducted in a lake shows significant improvement in the cancellation performance compared to the classical SRLS algorithm.</p> </pre></div></div>

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